Month: September 2024

Gaurav did Part A, Ankit did Part B and Bhavik did Part C

Currently, the most significant risk for the team is the viability of our Kalman filter approach to getting Euler angle measurements that don’t drift. In order for our PID to work, we need to have reliable 3DOF measurements and if we cannot correct for the natural integration drift of our gyro, we will not be able to get a stable drone. We believe we have found good resources online that walk us through the process of calibrating our noise in our Kalman filter and they use the same cheap IMU we are using, so we have confidence that we can solve this. Additionally, we are currently worried about getting high-fidelity measurements from our GPS as this depends on weather conditions outside, but there are ways to mitigate this such as setting up RTK base stations. We will have to empirically see if this is necessary based on outdoor testing.

We additionally decided to descope a lot of the custom components we were going to build. We decided that instead of designing and 3D printing our own chassis, we would buy an off-the-shelf carbon fiber frame. This will save us prototyping time and also will be a lot more durable than any 3D printing solution. Additionally, we decided to scrap the custom PCB in favor of a breadboard solution which should save us time in terms of prototyping for a PCB solution.

Part A:

Our product will meet a search and rescue operation need. This has extremely large impacts on safety and welfare. In terms of safety, this product will help search and rescue ships canvas a large amount of area in a much faster time. This means that search and rescue teams can deliver supplies and medical aid much faster and save lives. This will also satisfy the basic needs of people because in lower-income areas where they may not have access to robust search and rescue equipment, this drone will help teams maximize the resources they do have by locating the target much faster and more cost-effectively.

Part B:

With regards to social factors, our low cost solution enables increase in safety regardless of income level of the region. By focusing on a cost effective solution, we are enabling even impoverished regions that border large bodies of water to provide search and rescue. Communities are responsible for the safety of their people and as such, our product is enabling them to provide that regardless of resources that exist in the region

Part C:

Our project addresses the specific need of economic factors because we aim to reduce the overall cost and resources required to perform search and rescue operations. In traditional search and rescue missions, large teams, expensive equipment like helicopters, and significant fuel consumption all contribute to a high operational cost. Therefore, by including an autonomous drone-based solution, we can significantly reduce these costs. Out autonomous drones will allow these rescue missions to include fewer people and reduce the need for equipment such as helicopters. Additionally, the production and distribution of the drone will be open source and can be easily used by any team. Since the cost of the drone is also cheaper compared to traditional drones, it makes it easier for teams in lower-income areas to utilize.

Currently, the most significant risks that could jeopardize the success of the project are scheduling and training/calibration time. With scheduling, there are many deliverables and many components we need to ensure can work together. This could definitely jeopardize the success of the project if we have underestimated the development time. the other risk is training/calibration time. This time includes training the model and also calibrating the PID controller may take time to make sure it is stable and functions properly. If we are not able to calibrate it properly it could put the rest of the project at risk. These risks are being managed by ensuring the components of the task that could take the most amount of time are being done first and we are all working together to make it easier to deal with hurdles or obstacles. Since the calibration is unique to our drone, it is not possible for us to get around this part of the drone. We have to make sure this part functions properly. With the AI training, our contingency plan is to use an even simpler target that will be easier to spot.

There were some major changes made to the design of the system this week. We switched from the KV260 to the KR260 board. This change was necessary because another team needed the vision board so we switched. This actually reduces our cost as we no longer have to buy the accessory kit for the vision board. Another change that we made was using a pre-made drone chassis instead of 3d printing one. This change was necessary because this drone chassis we are buying has much of the power distribution and design done for us already, with a strong frame made out of carbon fiber. This will be more durable and solve our power distribution problems that we would have run into before. This will incur a cost of 60 dollars, but since this is a necessary cost, it will be mitigated by saving money in other areas. The final change we are making is our use case. We are narrowing down our use case to just over a body of water to detect people that are lost-at-sea which will make the efficacy of our drone much better.

No updated schedule required, everything is on track.